def boxleastsquares(time, relFlux, relFluxErr, acfBP):
model = BoxLeastSquares(time.values, relFlux.values, dy=relFluxErr.values)
duration = [20 / 1440, 40 / 1440, 80 / 1440, .1]
periodogram = model.power(period=[.5 * acfBP, acfBP, 2 * acfBP], duration=duration,
objective='snr')
period = periodogram.period
power = periodogram.power
maxPower = np.max(periodogram.power)
bestPeriod = periodogram.period[np.argmax(periodogram.power)]
return period, power, bestPeriod, maxPower
def calcBls(flux, time, bls_durs, minP=None, maxP=None, min_trans=3):
"""
Take a bls and return the spectrum.
"""
bls = BoxLeastSquares(time, flux)
period_grid = bls.autoperiod(bls_durs,minimum_period=minP, \
maximum_period=maxP, minimum_n_transit=min_trans, \
frequency_factor=0.8)
bls_power = bls.power(period_grid, bls_durs, oversample=20)
return bls_power
def period_finder(self,
nt=5,
min_p=1,
max_p=100,
n_f=10000,
auto=True,
method='LS_astropy'):
self.pmin = min_p
self.pmax = max_p
self.method = method
if self.method == 'LS_astropy':
if auto:
ls = LombScargle(self.df.t.values,
self.df.m.values,
self.df.e.values,
nterms=nt)
self.frequency, self.power = ls.autopower(
minimum_frequency=1. / self.pmax,
maximum_frequency=1. / self.pmin)
else:
self.frequency = np.linspace(1. / self.pmax, 1. / self.pmin,
n_f)
self.power = LombScargle(self.df.t.values, self.df.m.values,
self.df.e.values).power(
self.frequency)
elif self.method == 'BLS_astropy':
model = BoxLeastSquares(self.df.t.values * u.day,
self.df.m.values,
dy=self.df.e.values)
if auto:
periodogram = model.autopower(0.2)
self.frequency = 1. / periodogram.period
self.power = periodogram.power
else:
periods = np.linspace(self.pmin, self.pmax, 10)
periodogram = model.power(periods, 0.2)
self.frequency = 1. / periodogram.period
self.power = periodogram.power
else:
print('Method should be chosen between these options:')
print('LS_astropy, BLS_astropy')
sys.exit()
self.set_period()
self.plot_ls()
def __findBestSigma(self,
maxperiod,
window_length,
sigma_step,
sigma_max,
debug_mode=False):
i = 3 #starting point
self.sigArr = []
self.lenArr = []
self.maxdataArr = []
while (sigma_max >= i):
flat = self.rawlc.flatten(
window_length=window_length).remove_outliers(sigma=i)
model = BoxLeastSquares(flat.time, flat.flux, dy=0.01)
testperiods = np.arange(1, maxperiod, 0.001)
periodogram = model.power(testperiods, 0.16)
maxID = np.argmax(periodogram.power)
stat = model.compute_stats(periodogram.period[maxID],
periodogram.duration[maxID],
periodogram.transit_time[maxID])
self.sigArr.append(sum(stat['per_transit_log_likelihood']))
self.lenArr.append(len(stat['per_transit_log_likelihood']))
if debug_mode:
print([
i,
sum(stat['per_transit_log_likelihood']),
len(stat['per_transit_log_likelihood']),
periodogram.period[maxID]
]) #Debug
i += sigma_step
maxLLikeIndex = np.argwhere(self.lenArr == np.amax(self.lenArr))
for i in maxLLikeIndex:
self.maxdataArr.append(self.sigArr[i.item(0)])
bestFit = np.argmax(self.maxdataArr) - 1
if debug_mode:
print("Index of LogLikelihoods")
print(maxLLikeIndex)
print("Best Sigma")
print(bestFit + i)
return bestFit + i
def get_ref_vals(lightcurve, p_ref=None):
t = lightcurve.time
y = lightcurve.flux
dy = lightcurve.flux_err
bls = BoxLeastSquares(t, y, dy)
durations = [0.05, 0.1, 0.2]
if p_ref is None:
periodogram = bls.autopower(durations)
else:
periods = np.linspace(p_ref * 0.9, p_ref * 1.1, 5000)
periodogram = bls.power(periods, durations)
max_power = np.argmax(periodogram.power)
stats = bls.compute_stats(periodogram.period[max_power],
periodogram.duration[max_power],
periodogram.transit_time[max_power])
num_transits = len(stats['transit_times'])
t0 = periodogram.transit_time[max_power]
p = periodogram.period[max_power]
return (t0, p, num_transits)
def _create_interact_ui(doc,
minp=minimum_period,
maxp=maximum_period,
resolution=resolution):
"""Create BLS interact user interface."""
if minp is None:
minp = 0.3
if maxp is None:
maxp = (lc.time[-1] - lc.time[0]) / 2
time_format = ''
if lc.time_format == 'bkjd':
time_format = ' - 2454833 days'
if lc.time_format == 'btjd':
time_format = ' - 2457000 days'
# Some sliders
duration_slider = Slider(start=0.01,
end=0.5,
value=0.05,
step=0.01,
title="Duration [Days]",
width=400)
npoints_slider = Slider(start=500,
end=10000,
value=resolution,
step=100,
title="BLS Resolution",
width=400)
# Set up the period values, BLS model and best period
period_values = np.logspace(np.log10(minp), np.log10(maxp),
npoints_slider.value)
period_values = period_values[(period_values > duration_slider.value)
& (period_values < maxp)]
model = BoxLeastSquares(lc.time, lc.flux)
result = model.power(period_values, duration_slider.value)
loc = np.argmax(result.power)
best_period = result.period[loc]
best_t0 = result.transit_time[loc]
# Some Buttons
double_button = Button(label="Double Period",
button_type="danger",
width=100)
half_button = Button(label="Half Period",
button_type="danger",
width=100)
text_output = Paragraph(text="Period: {} days, T0: {}{}".format(
np.round(best_period, 7), np.round(best_t0, 7), time_format),
width=350,
height=40)
# Set up BLS source
bls_source = prepare_bls_datasource(result, loc)
bls_help_source = prepare_bls_help_source(bls_source,
npoints_slider.value)
# Set up the model LC
mf = model.model(lc.time, best_period, duration_slider.value, best_t0)
mf /= np.median(mf)
mask = ~(convolve(np.asarray(mf == np.median(mf)), Box1DKernel(2)) >
0.9)
model_lc = LightCurve(lc.time[mask], mf[mask])
model_lc = model_lc.append(
LightCurve([(lc.time[0] - best_t0) + best_period / 2], [1]))
model_lc = model_lc.append(
LightCurve([(lc.time[0] - best_t0) + 3 * best_period / 2], [1]))
model_lc_source = ColumnDataSource(
data=dict(time=np.sort(model_lc.time),
flux=model_lc.flux[np.argsort(model_lc.time)]))
# Set up the LC
nb = int(np.ceil(len(lc.flux) / 5000))
lc_source = prepare_lightcurve_datasource(lc[::nb])
lc_help_source = prepare_lc_help_source(lc)
# Set up folded LC
nb = int(np.ceil(len(lc.flux) / 10000))
f = lc.fold(best_period, best_t0)
f_source = prepare_folded_datasource(f[::nb])
f_help_source = prepare_f_help_source(f)
f_model_lc = model_lc.fold(best_period, best_t0)
f_model_lc = LightCurve([-0.5], [1]).append(f_model_lc)
f_model_lc = f_model_lc.append(LightCurve([0.5], [1]))
f_model_lc_source = ColumnDataSource(
data=dict(phase=f_model_lc.time, flux=f_model_lc.flux))
def _update_light_curve_plot(event):
"""If we zoom in on LC plot, update the binning."""
mint, maxt = fig_lc.x_range.start, fig_lc.x_range.end
inwindow = (lc.time > mint) & (lc.time < maxt)
nb = int(np.ceil(inwindow.sum() / 5000))
temp_lc = lc[inwindow]
lc_source.data = {
'time': temp_lc.time[::nb],
'flux': temp_lc.flux[::nb]
}
def _update_folded_plot(event):
loc = np.argmax(bls_source.data['power'])
best_period = bls_source.data['period'][loc]
best_t0 = bls_source.data['transit_time'][loc]
# Otherwise, we can just update the best_period index
minphase, maxphase = fig_folded.x_range.start, fig_folded.x_range.end
f = lc.fold(best_period, best_t0)
inwindow = (f.time > minphase) & (f.time < maxphase)
nb = int(np.ceil(inwindow.sum() / 10000))
f_source.data = {
'phase': f[inwindow].time[::nb],
'flux': f[inwindow].flux[::nb]
}
# Function to update the widget
def _update_params(all=False, best_period=None, best_t0=None):
if all:
# If we're updating everything, recalculate the BLS model
minp, maxp = fig_bls.x_range.start, fig_bls.x_range.end
period_values = np.logspace(np.log10(minp), np.log10(maxp),
npoints_slider.value)
ok = (period_values > duration_slider.value) & (period_values <
maxp)
if ok.sum() == 0:
return
period_values = period_values[ok]
result = model.power(period_values, duration_slider.value)
ok = np.isfinite(result['power']) & np.isfinite(result['duration']) &\
np.isfinite(result['transit_time']) & np.isfinite(result['period'])
bls_source.data = dict(period=result['period'][ok],
power=result['power'][ok],
duration=result['duration'][ok],
transit_time=result['transit_time'][ok])
loc = np.nanargmax(bls_source.data['power'])
best_period = bls_source.data['period'][loc]
best_t0 = bls_source.data['transit_time'][loc]
minpow, maxpow = bls_source.data['power'].min(
) * 0.95, bls_source.data['power'].max() * 1.05
fig_bls.y_range.start = minpow
fig_bls.y_range.end = maxpow
# Otherwise, we can just update the best_period index
minphase, maxphase = fig_folded.x_range.start, fig_folded.x_range.end
f = lc.fold(best_period, best_t0)
inwindow = (f.time > minphase) & (f.time < maxphase)
nb = int(np.ceil(inwindow.sum() / 10000))
f_source.data = {
'phase': f[inwindow].time[::nb],
'flux': f[inwindow].flux[::nb]
}
mf = model.model(lc.time, best_period, duration_slider.value,
best_t0)
mf /= np.median(mf)
mask = ~(convolve(np.asarray(mf == np.median(mf)), Box1DKernel(2))
> 0.9)
model_lc = LightCurve(lc.time[mask], mf[mask])
model_lc_source.data = {
'time': np.sort(model_lc.time),
'flux': model_lc.flux[np.argsort(model_lc.time)]
}
f_model_lc = model_lc.fold(best_period, best_t0)
f_model_lc = LightCurve([-0.5], [1]).append(f_model_lc)
f_model_lc = f_model_lc.append(LightCurve([0.5], [1]))
f_model_lc_source.data = {
'phase': f_model_lc.time,
'flux': f_model_lc.flux
}
vertical_line.update(location=best_period)
fig_folded.title.text = 'Period: {} days \t T0: {}{}'.format(
np.round(best_period, 7), np.round(best_t0, 7), time_format)
text_output.text = "Period: {} days, \t T0: {}{}".format(
np.round(best_period, 7), np.round(best_t0, 7), time_format)
# Callbacks
def _update_upon_period_selection(attr, old, new):
"""When we select a period we should just update a few things, but we should not recalculate model
"""
if len(new) > 0:
new = new[0]
best_period = bls_source.data['period'][new]
best_t0 = bls_source.data['transit_time'][new]
_update_params(best_period=best_period, best_t0=best_t0)
def _update_model_slider(attr, old, new):
"""If the duration slider is updated, then update the whole model set."""
_update_params(all=True)
def _update_model_slider_EVENT(event):
"""If we update the duration slider, we should update the whole model set.
This is the same as the _update_model_slider but it has a different call signature...
"""
_update_params(all=True)
def _double_period_event():
fig_bls.x_range.start *= 2
fig_bls.x_range.end *= 2
_update_params(all=True)
def _half_period_event():
fig_bls.x_range.start /= 2
fig_bls.x_range.end /= 2
_update_params(all=True)
# Help Hover Call Backs
def _update_folded_plot_help_reset(event):
f_help_source.data['phase'] = [
(np.max(f.time) - np.min(f.time)) * 0.98 + np.min(f.time)
]
f_help_source.data['flux'] = [
(np.max(f.flux) - np.min(f.flux)) * 0.98 + np.min(f.flux)
]
def _update_folded_plot_help(event):
f_help_source.data['phase'] = [
(fig_folded.x_range.end - fig_folded.x_range.start) * 0.95 +
fig_folded.x_range.start
]
f_help_source.data['flux'] = [
(fig_folded.y_range.end - fig_folded.y_range.start) * 0.95 +
fig_folded.y_range.start
]
def _update_lc_plot_help_reset(event):
lc_help_source.data['time'] = [
(np.max(lc.time) - np.min(lc.time)) * 0.98 + np.min(lc.time)
]
lc_help_source.data['flux'] = [
(np.max(lc.flux) - np.min(lc.flux)) * 0.9 + np.min(lc.flux)
]
def _update_lc_plot_help(event):
lc_help_source.data['time'] = [
(fig_lc.x_range.end - fig_lc.x_range.start) * 0.95 +
fig_lc.x_range.start
]
lc_help_source.data['flux'] = [
(fig_lc.y_range.end - fig_lc.y_range.start) * 0.9 +
fig_lc.y_range.start
]
def _update_bls_plot_help_event(event):
bls_help_source.data['period'] = [
bls_source.data['period'][int(npoints_slider.value * 0.95)]
]
bls_help_source.data['power'] = [
(np.max(bls_source.data['power']) -
np.min(bls_source.data['power'])) * 0.98 +
np.min(bls_source.data['power'])
]
def _update_bls_plot_help(attr, old, new):
bls_help_source.data['period'] = [
bls_source.data['period'][int(npoints_slider.value * 0.95)]
]
bls_help_source.data['power'] = [
(np.max(bls_source.data['power']) -
np.min(bls_source.data['power'])) * 0.98 +
np.min(bls_source.data['power'])
]
# Create all the figures.
fig_folded = make_folded_figure_elements(f, f_model_lc, f_source,
f_model_lc_source,
f_help_source)
fig_folded.title.text = 'Period: {} days \t T0: {}{}'.format(
np.round(best_period, 7), np.round(best_t0, 5), time_format)
fig_bls, vertical_line = make_bls_figure_elements(
result, bls_source, bls_help_source)
fig_lc = make_lightcurve_figure_elements(lc, model_lc, lc_source,
model_lc_source,
lc_help_source)
# Map changes
# If we click a new period, update
bls_source.selected.on_change('indices', _update_upon_period_selection)
# If we change the duration, update everything, including help button for BLS
duration_slider.on_change('value', _update_model_slider)
duration_slider.on_change('value', _update_bls_plot_help)
# If we increase resolution, update everything
npoints_slider.on_change('value', _update_model_slider)
# Make sure the vertical line always goes to the best period.
vertical_line.update(location=best_period)
# If we pan in the BLS panel, update everything
fig_bls.on_event(PanEnd, _update_model_slider_EVENT)
fig_bls.on_event(Reset, _update_model_slider_EVENT)
# If we pan in the LC panel, rebin the points
fig_lc.on_event(PanEnd, _update_light_curve_plot)
fig_lc.on_event(Reset, _update_light_curve_plot)
# If we pan in the Folded panel, rebin the points
fig_folded.on_event(PanEnd, _update_folded_plot)
fig_folded.on_event(Reset, _update_folded_plot)
# Deal with help button
fig_bls.on_event(PanEnd, _update_bls_plot_help_event)
fig_bls.on_event(Reset, _update_bls_plot_help_event)
fig_folded.on_event(PanEnd, _update_folded_plot_help)
fig_folded.on_event(Reset, _update_folded_plot_help_reset)
fig_lc.on_event(PanEnd, _update_lc_plot_help)
fig_lc.on_event(Reset, _update_lc_plot_help_reset)
# Buttons
double_button.on_click(_double_period_event)
half_button.on_click(_half_period_event)
# Layout the widget
doc.add_root(
layout([[fig_bls, fig_folded], fig_lc,
[
Spacer(width=70), duration_slider,
Spacer(width=50), npoints_slider
],
[
Spacer(width=70), double_button,
Spacer(width=70), half_button,
Spacer(width=300), text_output
]]))
def __generatePeriodogram(self, maxperiod):
model = BoxLeastSquares(self.flat.time, self.flat.flux, dy=0.01)
testperiods = np.arange(1, maxperiod, 0.001)
self.periodogram = model.power(testperiods, 0.16)
class BLSStep(OTSStep):
name = "bls"
def __init__(self, ts):
super().__init__(ts)
self._periods = None
self._durations = array([0.25, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0
]) / 24
self.bls = None
self.result = None
self.period = None # Best-fit period
self.zero_epoch = None # Best-fit zero epoch
self.duration = None # Best-fit duration
self.depth = None # Best-fit depth
self.snr = None # Best-fit Signal to noise ratio
def __call__(self, *args, **kwargs):
self.logger = getLogger(
f"{self.name}:{self.ts.name.lower().replace('_','-')}")
self.logger.info("Running BLS periodogram")
self._periods = linspace(self.ts.pmin, self.ts.pmax, self.ts.nper)
self.bls = BoxLeastSquares(self.ts.time * u.day, self.ts.flux,
self.ts.ferr)
self.result = self.bls.power(self._periods,
self._durations,
objective='snr')
for p in self.ts.masked_periods:
self.result.depth_snr *= maskf(self._periods, p, .1)
self.result.log_likelihood *= maskf(self._periods, p, .1)
i = argmax(self.result.depth_snr)
self.period = self.result.period[i].value
self.snr = self.result.depth_snr[i]
self.duration = self.result.duration[i].value
self.depth = self.result.depth[i]
t0 = self.result.transit_time[i].value
ep = epoch(self.ts.time.min(), t0, self.period)
self.zero_epoch = t0 + ep * self.period
self.ts.update_ephemeris(self.zero_epoch, self.period, self.duration,
self.depth)
self.logger.info(
f"BLS SNR {self.snr:.2f} period {self.period:.2f} d, duration {24*self.duration:.2f} h"
)
def add_to_fits(self, hdul: HDUList):
if self.bls is not None:
h = hdul[0].header
h.append(Card('COMMENT', '======================'))
h.append(Card('COMMENT', ' BLS results '))
h.append(Card('COMMENT', '======================'))
h.append(Card('bls_snr', self.snr,
'BLS depth signal to noise ratio'),
bottom=True)
h.append(Card('period', self.period, 'Orbital period [d]'),
bottom=True)
h.append(Card('epoch', self.zero_epoch, 'Zero epoch [BJD]'),
bottom=True)
h.append(Card('duration', self.duration, 'Transit duration [d]'),
bottom=True)
h.append(Card('depth', self.depth, 'Transit depth'), bottom=True)
@bplot
def plot_snr(self, ax=None):
if self.period < 1.:
ax.semilogx(self._periods,
self.result.depth_snr,
drawstyle='steps-mid')
else:
ax.plot(self._periods,
self.result.depth_snr,
drawstyle='steps-mid')
ax.axvline(self.period,
alpha=0.15,
c='orangered',
ls='-',
lw=10,
zorder=-100)
setp(ax, xlabel='Period [d]', ylabel='Depth SNR')
ax.autoscale(axis='x', tight=True)
def from_lightcurve(lc, **kwargs):
"""Creates a Periodogram from a LightCurve using the Box Least Squares (BLS) method."""
# BoxLeastSquares was added to `astropy.stats` in AstroPy v3.1 and then
# moved to `astropy.timeseries` in v3.2, which makes the import below
# somewhat complicated.
try:
from astropy.timeseries import BoxLeastSquares
except ImportError:
try:
from astropy.stats import BoxLeastSquares
except ImportError:
raise ImportError("BLS requires AstroPy v3.1 or later")
# Validate user input for `lc`
# (BoxLeastSquares will not work if flux or flux_err contain NaNs)
lc = lc.remove_nans()
if np.isfinite(lc.flux_err).all():
dy = lc.flux_err
else:
dy = None
# Validate user input for `duration`
duration = kwargs.pop("duration", 0.25)
if duration is not None and ~np.all(np.isfinite(duration)):
raise ValueError("`duration` parameter contains illegal nan or inf value(s)")
# Validate user input for `period`
period = kwargs.pop("period", None)
minimum_period = kwargs.pop("minimum_period", None)
maximum_period = kwargs.pop("maximum_period", None)
if period is not None and ~np.all(np.isfinite(period)):
raise ValueError("`period` parameter contains illegal nan or inf value(s)")
if minimum_period is None:
if period is None:
minimum_period = np.max([np.median(np.diff(lc.time)) * 4,
np.max(duration) + np.median(np.diff(lc.time))])
else:
minimum_period = np.min(period)
if maximum_period is None:
if period is None:
maximum_period = (np.max(lc.time) - np.min(lc.time)) / 3.
else:
maximum_period = np.max(period)
# Validate user input for `time_unit`
time_unit = (kwargs.pop("time_unit", "day"))
if time_unit not in dir(u):
raise ValueError('{} is not a valid value for `time_unit`'.format(time_unit))
# Validate user input for `frequency_factor`
frequency_factor = kwargs.pop("frequency_factor", 10)
df = frequency_factor * np.min(duration) / (np.max(lc.time) - np.min(lc.time))**2
npoints = int(((1/minimum_period) - (1/maximum_period))/df)
if npoints > 1e7:
raise ValueError('`period` contains {} points.'
'Periodogram is too large to evaluate. '
'Consider setting `frequency_factor` to a higher value.'
''.format(np.round(npoints, 4)))
elif npoints > 1e5:
log.warning('`period` contains {} points.'
'Periodogram is likely to be large, and slow to evaluate. '
'Consider setting `frequency_factor` to a higher value.'
''.format(np.round(npoints, 4)))
# Create BLS object and run the BLS search
bls = BoxLeastSquares(lc.time, lc.flux, dy)
if period is None:
period = bls.autoperiod(duration,
minimum_period=minimum_period,
maximum_period=maximum_period,
frequency_factor=frequency_factor)
result = bls.power(period, duration, **kwargs)
if not isinstance(result.period, u.quantity.Quantity):
result.period = u.Quantity(result.period, time_unit)
if not isinstance(result.power, u.quantity.Quantity):
result.power = result.power * u.dimensionless_unscaled
if not isinstance(result.duration, u.quantity.Quantity):
result.duration = u.Quantity(result.duration, time_unit)
return BoxLeastSquaresPeriodogram(frequency=1. / result.period,
power=result.power,
default_view='period',
label=lc.label,
targetid=lc.targetid,
transit_time=result.transit_time,
duration=result.duration,
depth=result.depth,
bls_result=result,
snr=result.depth_snr,
bls_obj=bls,
time=lc.time,
flux=lc.flux,
time_unit=time_unit)
def find_and_mask_transits(time,
flux,
flux_err,
periods,
durations,
nplanets=1,
plot=False):
"""
Iteratively find and mask transits in the flattened light curve.
Args:
time (array): The time array.
flux (array): The flux array. You'll get the best results
if this is flattened.
flux_err (array): The array of flux uncertainties.
periods (array): The array of periods to search over for BLS.
For example, periods = np.linspace(0.5, 20, 10)
durations (array): The array of durations to search over for BLS.
For example, durations = np.linspace(0.05, 0.2, 10)
nplanets (Optional[int]): The number of planets you'd like to search for.
This function will interatively find and remove nplanets. Default is 1.
Returns:
transit_masks (list): a list of masks that correspond to the in
transit points of each light curve. To mask out transits do
time[~transit_masks[index]], etc.
"""
cum_transit = np.ones(len(time), dtype=bool)
_time, _flux, _flux_err = time * 1, flux * 1, flux_err * 1
t0s, durs, porbs = [np.zeros(nplanets) for i in range(3)]
transit_masks = []
for i in range(nplanets):
bls = BoxLeastSquares(t=_time, y=_flux, dy=_flux_err)
bls.power(periods, durations)
periods = bls.autoperiod(durations,
minimum_n_transit=3,
frequency_factor=5.0)
results = bls.autopower(durations, frequency_factor=5.0)
# Find the period of the peak
period = results.period[np.argmax(results.power)]
# Extract the parameters of the best-fit model
index = np.argmax(results.power)
porbs[i] = results.period[index]
t0s[i] = results.transit_time[index]
durs[i] = results.duration[index]
if plot:
# Plot the periodogram
fig, ax = plt.subplots(1, 1, figsize=(10, 5))
ax.plot(results.period, results.power, "k", lw=0.5)
ax.set_xlim(results.period.min(), results.period.max())
ax.set_xlabel("period [days]")
ax.set_ylabel("log likelihood")
# Highlight the harmonics of the peak period
ax.axvline(period, alpha=0.4, lw=4)
for n in range(2, 10):
ax.axvline(n * period, alpha=0.4, lw=1, linestyle="dashed")
ax.axvline(period / n, alpha=0.4, lw=1, linestyle="dashed")
# plt.show()
# plt.plot(_time, _flux, ".")
# plt.xlim(1355, 1360)
in_transit = bls.transit_mask(_time, porbs[i], durs[i], t0s[i])
transit_masks.append(in_transit)
_time, _flux, _flux_err = _time[~in_transit], _flux[~in_transit], \
_flux_err[~in_transit]
return transit_masks, t0s, durs, porbs
def _create_interact_ui(doc,
minp=minimum_period,
maxp=maximum_period,
resolution=resolution):
"""Create BLS interact user interface."""
if minp is None:
minp = 0.3
if maxp is None:
maxp = (lc.time[-1].value - lc.time[0].value) / 2
# TODO: consider to accept Time as minp / maxp, and convert it to unitless days
time_format = ""
if lc.time.format == "bkjd":
time_format = " - 2454833 days"
if lc.time.format == "btjd":
time_format = " - 2457000 days"
# Some sliders
duration_slider = Slider(
start=0.01,
end=0.5,
value=0.05,
step=0.01,
title="Duration [Days]",
width=400,
)
npoints_slider = Slider(
start=500,
end=10000,
value=resolution,
step=100,
title="BLS Resolution",
width=400,
)
# Set up the period values, BLS model and best period
period_values = np.logspace(np.log10(minp), np.log10(maxp),
npoints_slider.value)
period_values = period_values[(period_values > duration_slider.value)
& (period_values < maxp)]
model = BoxLeastSquares(lc.time, lc.flux)
result = model.power(period_values, duration_slider.value)
loc = np.argmax(result.power)
best_period = result.period[loc]
best_t0 = result.transit_time[loc]
# Some Buttons
double_button = Button(label="Double Period",
button_type="danger",
width=100)
half_button = Button(label="Half Period",
button_type="danger",
width=100)
text_output = Paragraph(
text="Period: {} days, T0: {}{}".format(
_round_strip_unit(best_period, 7),
_round_strip_unit(best_t0, 7),
time_format,
),
width=350,
height=40,
)
# Set up BLS source
bls_source = prepare_bls_datasource(result, loc)
bls_source_units = dict(
transit_time_format=result["transit_time"].format,
transit_time_scale=result["transit_time"].scale,
period=result["period"].unit,
)
bls_help_source = prepare_bls_help_source(bls_source,
npoints_slider.value)
# Set up the model LC
mf = model.model(lc.time, best_period, duration_slider.value, best_t0)
mf /= np.median(mf)
mask = ~(convolve(np.asarray(mf == np.median(mf)), Box1DKernel(2)) >
0.9)
model_lc = _to_lc(lc.time[mask], mf[mask])
model_lc_source = _to_ColumnDataSource(
data=dict(time=model_lc.time, flux=model_lc.flux))
# Set up the LC
nb = int(np.ceil(len(lc.flux) / 5000))
lc_source = prepare_lightcurve_datasource(lc[::nb])
lc_help_source = prepare_lc_help_source(lc)
# Set up folded LC
nb = int(np.ceil(len(lc.flux) / 10000))
f = lc.fold(best_period, best_t0)
f_source = prepare_folded_datasource(f[::nb])
f_help_source = prepare_f_help_source(f)
f_model_lc = model_lc.fold(best_period, best_t0)
f_model_lc = _to_lc(_as_1d(f.time.min()), [1]).append(f_model_lc)
f_model_lc = f_model_lc.append(_to_lc(_as_1d(f.time.max()), [1]))
f_model_lc_source = _to_ColumnDataSource(
data=dict(phase=f_model_lc.time, flux=f_model_lc.flux))
def _update_light_curve_plot(event):
"""If we zoom in on LC plot, update the binning."""
mint, maxt = fig_lc.x_range.start, fig_lc.x_range.end
inwindow = (lc.time.value > mint) & (lc.time.value < maxt)
nb = int(np.ceil(inwindow.sum() / 5000))
temp_lc = lc[inwindow]
_update_source(lc_source, {
"time": temp_lc.time[::nb],
"flux": temp_lc.flux[::nb]
})
def _update_folded_plot(event):
loc = np.argmax(bls_source.data["power"])
best_period = bls_source.data["period"][loc]
best_t0 = bls_source.data["transit_time"][loc]
# Otherwise, we can just update the best_period index
minphase, maxphase = fig_folded.x_range.start, fig_folded.x_range.end
f = lc.fold(best_period, best_t0)
inwindow = (f.time > minphase) & (f.time < maxphase)
nb = int(np.ceil(inwindow.sum() / 10000))
_update_source(
f_source,
{
"phase": f[inwindow].time[::nb],
"flux": f[inwindow].flux[::nb]
},
)
# Function to update the widget
def _update_params(all=False, best_period=None, best_t0=None):
if all:
# If we're updating everything, recalculate the BLS model
minp, maxp = fig_bls.x_range.start, fig_bls.x_range.end
period_values = np.logspace(np.log10(minp), np.log10(maxp),
npoints_slider.value)
ok = (period_values > duration_slider.value) & (period_values <
maxp)
if ok.sum() == 0:
return
period_values = period_values[ok]
result = model.power(period_values, duration_slider.value)
ok = (_isfinite(result["power"])
& _isfinite(result["duration"])
& _isfinite(result["transit_time"])
& _isfinite(result["period"]))
ok_result = dict(
period=result["period"]
[ok], # useful for accessing values with units needed later
power=result["power"][ok],
duration=result["duration"][ok],
transit_time=result["transit_time"][ok],
)
_update_source(bls_source, ok_result)
loc = np.nanargmax(ok_result["power"])
best_period = ok_result["period"][loc]
best_t0 = ok_result["transit_time"][loc]
minpow, maxpow = (
bls_source.data["power"].min() * 0.95,
bls_source.data["power"].max() * 1.05,
)
fig_bls.y_range.start = minpow
fig_bls.y_range.end = maxpow
# Otherwise, we can just update the best_period index
minphase, maxphase = fig_folded.x_range.start, fig_folded.x_range.end
f = lc.fold(best_period, best_t0)
inwindow = (f.time > minphase) & (f.time < maxphase)
nb = int(np.ceil(inwindow.sum() / 10000))
_update_source(
f_source,
{
"phase": f[inwindow].time[::nb],
"flux": f[inwindow].flux[::nb]
},
)
mf = model.model(lc.time, best_period, duration_slider.value,
best_t0)
mf /= np.median(mf)
mask = ~(convolve(np.asarray(mf == np.median(mf)), Box1DKernel(2))
> 0.9)
model_lc = _to_lc(lc.time[mask], mf[mask])
_update_source(model_lc_source, {
"time": model_lc.time,
"flux": model_lc.flux
})
f_model_lc = model_lc.fold(best_period, best_t0)
f_model_lc = _to_lc(_as_1d(f.time.min()), [1]).append(f_model_lc)
f_model_lc = f_model_lc.append(_to_lc(_as_1d(f.time.max()), [1]))
_update_source(f_model_lc_source, {
"phase": f_model_lc.time,
"flux": f_model_lc.flux
})
vertical_line.update(location=best_period.value)
fig_folded.title.text = "Period: {} days \t T0: {}{}".format(
_round_strip_unit(best_period, 7),
_round_strip_unit(best_t0, 7),
time_format,
)
text_output.text = "Period: {} days, \t T0: {}{}".format(
_round_strip_unit(best_period, 7),
_round_strip_unit(best_t0, 7),
time_format,
)
# Callbacks
def _update_upon_period_selection(attr, old, new):
"""When we select a period we should just update a few things, but we should not recalculate model"""
if len(new) > 0:
new = new[0]
best_period = (bls_source.data["period"][new] *
bls_source_units["period"])
best_t0 = Time(
bls_source.data["transit_time"][new],
format=bls_source_units["transit_time_format"],
scale=bls_source_units["transit_time_scale"],
)
_update_params(best_period=best_period, best_t0=best_t0)
def _update_model_slider(attr, old, new):
"""If the duration slider is updated, then update the whole model set."""
_update_params(all=True)
def _update_model_slider_EVENT(event):
"""If we update the duration slider, we should update the whole model set.
This is the same as the _update_model_slider but it has a different call signature...
"""
_update_params(all=True)
def _double_period_event():
fig_bls.x_range.start *= 2
fig_bls.x_range.end *= 2
_update_params(all=True)
def _half_period_event():
fig_bls.x_range.start /= 2
fig_bls.x_range.end /= 2
_update_params(all=True)
# Help Hover Call Backs
def _update_folded_plot_help_reset(event):
f_help_source.data["phase"] = [_at_ratio(f.time, 0.95)]
f_help_source.data["flux"] = [_at_ratio(f.flux, 0.95)]
def _update_folded_plot_help(event):
f_help_source.data["phase"] = [_at_ratio(fig_folded.x_range, 0.95)]
f_help_source.data["flux"] = [_at_ratio(fig_folded.y_range, 0.95)]
def _update_lc_plot_help_reset(event):
lc_help_source.data["time"] = [_at_ratio(lc.time, 0.98)]
lc_help_source.data["flux"] = [_at_ratio(lc.flux, 0.95)]
def _update_lc_plot_help(event):
lc_help_source.data["time"] = [_at_ratio(fig_lc.x_range, 0.98)]
lc_help_source.data["flux"] = [_at_ratio(fig_lc.y_range, 0.95)]
def _update_bls_plot_help_event(event):
# cannot use _at_ratio helper for period, because period is log scaled.
bls_help_source.data["period"] = [
bls_source.data["period"][int(npoints_slider.value * 0.95)]
]
bls_help_source.data["power"] = [
_at_ratio(bls_source.data["power"], 0.98)
]
def _update_bls_plot_help(attr, old, new):
bls_help_source.data["period"] = [
bls_source.data["period"][int(npoints_slider.value * 0.95)]
]
bls_help_source.data["power"] = [
_at_ratio(bls_source.data["power"], 0.98)
]
# Create all the figures.
fig_folded = make_folded_figure_elements(f, f_model_lc, f_source,
f_model_lc_source,
f_help_source)
fig_folded.title.text = "Period: {} days \t T0: {}{}".format(
_round_strip_unit(best_period, 7),
_round_strip_unit(best_t0, 5),
time_format,
)
fig_bls, vertical_line = make_bls_figure_elements(
result, bls_source, bls_help_source)
fig_lc = make_lightcurve_figure_elements(lc, model_lc, lc_source,
model_lc_source,
lc_help_source)
# Map changes
# If we click a new period, update
bls_source.selected.on_change("indices", _update_upon_period_selection)
# If we change the duration, update everything, including help button for BLS
duration_slider.on_change("value", _update_model_slider)
duration_slider.on_change("value", _update_bls_plot_help)
# If we increase resolution, update everything
npoints_slider.on_change("value", _update_model_slider)
# Make sure the vertical line always goes to the best period.
vertical_line.update(location=best_period.value)
# If we pan in the BLS panel, update everything
fig_bls.on_event(PanEnd, _update_model_slider_EVENT)
fig_bls.on_event(Reset, _update_model_slider_EVENT)
# If we pan in the LC panel, rebin the points
fig_lc.on_event(PanEnd, _update_light_curve_plot)
fig_lc.on_event(Reset, _update_light_curve_plot)
# If we pan in the Folded panel, rebin the points
fig_folded.on_event(PanEnd, _update_folded_plot)
fig_folded.on_event(Reset, _update_folded_plot)
# Deal with help button
fig_bls.on_event(PanEnd, _update_bls_plot_help_event)
fig_bls.on_event(Reset, _update_bls_plot_help_event)
fig_folded.on_event(PanEnd, _update_folded_plot_help)
fig_folded.on_event(Reset, _update_folded_plot_help_reset)
fig_lc.on_event(PanEnd, _update_lc_plot_help)
fig_lc.on_event(Reset, _update_lc_plot_help_reset)
# Buttons
double_button.on_click(_double_period_event)
half_button.on_click(_half_period_event)
# Layout the widget
doc.add_root(
layout([
[fig_bls, fig_folded],
fig_lc,
[
Spacer(width=70),
duration_slider,
Spacer(width=50),
npoints_slider,
],
[
Spacer(width=70),
double_button,
Spacer(width=70),
half_button,
Spacer(width=300),
text_output,
],
]))
def _package_results(
tpf,
target,
contaminator,
aper,
contaminant_aper,
transit_pixels,
transit_pixels_err,
period,
t0,
duration,
plot=False,
):
"""Helper function for packaging up results"""
# def get_coords(thumb, err, aper, count=400):
# Y, X = np.mgrid[: tpf.shape[1], : tpf.shape[2]]
# cxs, cys = [], []
# for count in range(count):
# err1 = np.random.normal(0, err[aper])
# cxs.append(np.average(X[aper], weights=thumb[aper] + err1))
# cys.append(np.average(Y[aper], weights=thumb[aper] + err1))
# cxs, cys = np.asarray(cxs), np.asarray(cys)
# cras, cdecs = tpf.wcs.wcs_pix2world(np.asarray([cxs, cys]).T, 1).T
# return cras, cdecs
def get_coords(thumb, err, aper=None):
if aper is None:
aper = np.ones(tpf.flux.shape[1:], bool)
with np.errstate(divide="ignore"):
Y, X = np.mgrid[:tpf.shape[1], :tpf.shape[2]]
aper = create_threshold_mask(thumb / err, 3) & aper
cxs, cys = [], []
for count in range(500):
w = np.random.normal(loc=np.abs(thumb[aper]), scale=err[aper])
cxs.append(np.average(X[aper], weights=w))
cys.append(np.average(Y[aper], weights=w))
cxs, cys = np.asarray(cxs), np.asarray(cys)
k = (cxs > 0) & (cxs < tpf.shape[2]) & (cys > 0) & (cys <
tpf.shape[1])
cxs, cys = cxs[k], cys[k]
cras, cdecs = tpf.wcs.all_pix2world(np.asarray([cxs, cys]).T, 1).T
return cras, cdecs
thumb = np.nanmean(np.nan_to_num(tpf.flux.value), axis=0)
err = (np.sum(np.nan_to_num(tpf.flux_err.value)**2, axis=0)**0.5) / len(
tpf.time)
ra_target, dec_target = get_coords(thumb, err, aper=aper)
bls = BoxLeastSquares(target.time, target.flux, target.flux_err)
depths = []
for i in range(50):
bls.y = target.flux + np.random.normal(0, target.flux_err)
depths.append(bls.power(period, duration)["depth"][0])
target_depth = (np.mean(depths), np.std(depths))
res = {"target_depth": target_depth}
res["target_ra"] = np.median(ra_target), np.std(ra_target)
res["target_dec"] = np.median(dec_target), np.std(dec_target)
res["target_lc"] = target
res["target_aper"] = aper
if contaminant_aper.any():
ra_contaminant, dec_contaminant = get_coords(transit_pixels,
transit_pixels_err)
bls = BoxLeastSquares(contaminator.time, contaminator.flux,
contaminator.flux_err)
depths = []
for i in range(50):
bls.y = contaminator.flux + np.random.normal(
0, contaminator.flux_err)
depths.append(bls.power(period, duration)["depth"][0])
contaminator_depth = (np.mean(depths), np.std(depths))
res["contaminator_ra"] = np.median(ra_contaminant), np.std(
ra_contaminant)
res["contaminator_dec"] = np.median(dec_contaminant), np.std(
dec_contaminant)
res["contaminator_depth"] = contaminator_depth
res["contaminator_lc"] = contaminator
res["contaminator_aper"] = contaminant_aper
d, de = (contaminator_depth[0] - target_depth[0]), np.hypot(
contaminator_depth[1], target_depth[1])
res["delta_transit_depth[sigma]"] = d / de
dra = res["contaminator_ra"][0] - res["target_ra"][0]
ddec = res["contaminator_dec"][0] - res["target_dec"][0]
edra = (res["contaminator_ra"][1]**2 + res["target_ra"][1]**2)**0.5
eddec = (res["contaminator_dec"][1]**2 + res["target_dec"][1]**2)**0.5
centroid_shift = (((dra**2 + ddec**2)**0.5) * u.deg).to(u.arcsecond)
ecentroid_shift = (centroid_shift * ((2 * edra / dra)**2 +
(2 * eddec / ddec)**2)**0.5)
res["centroid_shift"] = (centroid_shift, ecentroid_shift)
res["period"] = period
res["t0"] = t0
res["duration"] = duration
res["transit_depth"] = transit_pixels
res["transit_depth_err"] = transit_pixels_err
if plot:
res["fig"] = _make_plot(tpf, res)
return res
def run_BLS(fl):
t, f, e = np.genfromtxt(fl, usecols=(0,1,2), unpack=True)
mask = cleaner(t,f)
t = t[~mask]
f = f[~mask]
e = e[~mask]
lc = TessLightCurve(time=t, flux=f, flux_err=e).flatten(window_length=51, polyorder=2, niters=5)
#Test Fill
'''
diffs = np.diff(lc.time)
stdd = np.nanstd(diffs)
medd = np.nanmedian(diffs)
maskgaps = diffs > 0.2#np.abs(diffs-medd) > stdd
maskgaps = np.concatenate((maskgaps,[False]))
'''
'''
for mg in np.where(maskgaps)[0]:
addtime = np.arange(lc.time[mg]+0.05, lc.time[mg+1], 0.05)
addflux = np.random.normal(1, 8e-4, len(addtime))
lc.time = np.concatenate((lc.time, addtime))
lc.flux = np.concatenate((lc.flux, addflux))
addorder = np.argsort(lc.time)
lc.time = lc.time[addorder]
lc.flux = lc.flux[addorder]
'''
#fmed = np.nanmedian(lc.flux)
#fstd = np.nanstd(lc.flux)
#stdm = lc.flux < 0.97#np.abs(lc.flux-fmed) > 3*fstd
periods = np.exp(np.linspace(np.log(args.min_period), np.log(args.max_period), 5000))
durations = np.linspace(0.05, 0.15, 20)# * u.day
model = BLS(lc.time,lc.flux) if not args.TLS else transitleastsquares(lc.time.value, lc.flux, lc.flux_err)
#result = model.power(periods, durations, oversample=20)#, objective='snr')
result = model.power(period_min=args.min_period, oversampling_factor=2, n_transits_min=1, use_threads=1, show_progress_bar=False)
#try:
#result = model.autopower(durations, frequency_factor=2.0, maximum_period=args.max_period)
#except:
# print(fl)
idx = np.argmax(result.power)
period = result.period[idx]
t0 = result.transit_time[idx]
dur = result.duration[idx]
depth = result.depth[idx]
snr = result.depth_snr[idx]
'''
period = result.period
t0 = result.T0
dur = result.duration
depth = 1 - result.depth
snr = result.snr
'''
try:
stats = model.compute_stats(period, dur, t0)
depth_even = stats['depth_even'][0]
depth_odd = stats['depth_odd'][0]
depth_half = stats['depth_half'][0]
t0, t1 = stats['transit_times'][:2]
ntra = len(stats['transit_times'])
except:
depth_even = 0
depth_odd = 0
depth_half = 0
t1 = 0
ntra = 0
if args.target is not None:
return fl, period, t0, dur, depth, snr, depth_even, depth_odd, depth_half, t1, ntra, result.period, result.power, lc.time, lc.flux, diffs
else:
return fl, period, t0, dur, depth, snr, depth_even, depth_odd, depth_half, t1, ntra
